Dataset Information

Genetic Landscape of Prostate Cancer Conspicuity on Multiparametric Magnetic Resonance Imaging: A Systematic Review and Bioinformatic Analysis.

ABSTRACT:

Context

Multiparametric magnetic resonance imaging (mpMRI) detects most, but not all, clinically significant prostate cancer. The genetic basis of prostate cancer visibility and invisibility on mpMRI remains uncertain.

Objective

To systematically review the literature on differential gene expression between mpMRI-visible and mpMRI-invisible prostate cancer, and to use bioinformatic analysis to identify enriched processes or cellular components in genes validated in more than one study.

Evidence acquisition

We performed a systematic literature search of the Medline, EMBASE, PubMed, and Cochrane databases up to January 2020 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The primary endpoint was differential genetic features between mpMRI-visible and mpMRI-invisible tumours. Secondary endpoints were explanatory links between gene function and mpMRI conspicuity, and the prognostic value of differential gene enrichment.

Evidence synthesis

We retrieved 445 articles, of which 32 met the criteria for inclusion. Thematic synthesis from the included studies showed that mpMRI-visible cancer tended towards enrichment of molecular features associated with increased disease aggressivity, including phosphatase and tensin homologue (PTEN) loss and higher genomic classifier scores, such as Oncotype and Decipher. Three of the included studies had accompanying publicly available data suitable for further bioinformatic analysis. An over-representation analysis of these datasets revealed increased expression of genes associated with extracellular matrix components in mpMRI-visible tumours.

Conclusions

Prostate cancer that is visible on mpMRI is generally enriched with molecular features of tumour development and aggressivity, including activation of proliferative signalling, DNA damage, and inflammatory processes. Additionally, there appears to be concordant cellular components and biological processes associated with mpMRI conspicuity, as highlighted by bioinformatic analysis of large genetic datasets.

Patient summary

Prostate cancer that is detected by magnetic resonance imaging (MRI) tends to have genetic features that are associated with more aggressive disease. This suggests that MRI can be used to assess the likelihood of aggressive prostate cancer, based on tumour visibility.

SUBMITTER: Norris JM

PROVIDER: S-EPMC7497895 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeThe clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans.MethodsWe retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: "negative", "equivocal", and "suspicious" for the presence of PCa.ResultsUnivariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case.ConclusionOur multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.

Project description:There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng∕ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accurately localizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.